Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
Research
Do baby boomers use more healthcare
services than other generations?
Longitudinal trajectories of physician
service use across five birth cohorts
Mayilee Canizares,1,2 Monique Gignac,2,3 Sheilah Hogg-Johnson,3,4
Richard H Glazier,5,6,7 Elizabeth M Badley2,4
To cite: Canizares M,
Gignac M, Hogg-Johnson S,
et al. Do baby boomers use
more healthcare services than
other generations?
Longitudinal trajectories of
physician service use across
five birth cohorts. BMJ Open
2016;6:e013276.
doi:10.1136/bmjopen-2016013276
▸ Prepublication history and
additional material is
available. To view please visit
the journal (http://dx.doi.org/
10.1136/bmjopen-2016013276).
Received 30 June 2016
Revised 22 August 2016
Accepted 26 August 2016
For numbered affiliations see
end of article.
Correspondence to
Mayilee Canizares;
[email protected]
ABSTRACT
Objective: In light of concerns for meeting the
provision of healthcare services given the large
numbers of ageing baby boomers, we compared the
trajectories of primary care and specialist services use
across the lifecourse of 5 birth cohorts and examined
factors associated with birth cohort differences.
Design: Longitudinal panel.
Setting: Canadian National Population Health Survey
(1994–2011).
Population: Sample of 10 186 individuals aged 20–
69 years in 1994–1995 and who were from 5 birth
cohorts: Generation X (Gen X; born: 1965–1974),
Younger Baby Boomers (born: 1955–1964), Older
Baby Boomers (born: 1945–1954), World War II (born:
1935–1944) and pre-World War II (born: 1925–1934).
Main outcomes: Use of primary care and specialist
services.
Results: Although the overall pattern suggested less
use of physician services by each successive recent
cohort, this blinded differences in primary and
specialist care use by cohort. Multilevel analyses
comparing cohorts showed that Gen Xers and younger
boomers, particularly those with multimorbidity, were
less likely to use primary care than earlier cohorts. In
contrast, specialist use was higher in recent cohorts,
with Gen Xers having the highest specialist use.
These increases were explained by the increasing
levels of multimorbidity. Education, income, having a
regular source of care, sedentary lifestyle and obesity
were significantly associated with physician services
use, but only partially contributed to cohort
differences.
Conclusions: The findings suggest a shift from
primary care to specialist care among recent cohorts,
particularly for those with multimorbidity. This is of
concern given policies to promote primary care
services to prevent and manage chronic conditions.
There is a need for policies to address important
generational differences in healthcare preferences and
the balance between primary and specialty care to
ensure integration and coordination of healthcare
delivery.
Strengths and limitations of this study
▪ No study has compared the patterns of primary
care and specialist service use among baby
boomers and other generations.
▪ Large longitudinal data, spanning 18 years,
enabled us to compare different cohorts at the
same chronological age.
▪ Our analytical methodology integrated changes
in healthcare use indicators with changes in
factors associated with them.
▪ The interpretation of the findings is limited due
to the inability to identify the specific conditions
for which individuals are consulting with
physicians.
▪ The data are self-reported and the bias associated with inaccuracies and reporting errors is
unknown.
INTRODUCTION
Older age is typically associated with worse
health, higher healthcare use1–3 and
increased healthcare costs.4–6 Consequently,
the large number of ageing baby boomers
(born 1945–1964), who are now 50+ years of
age, are generating concerns for the provision of health services in North America and
elsewhere. Two issues have been raised: the
large size of the cohort and the belief that
baby boomers are different in their needs
and attitudes towards healthcare from their
predecessors.7–11 Baby boomers grew up at a
time of social change, economic growth and
prosperity with improved access to education,
employment opportunities, and with access
to health and welfare services.12–14 They are
the first generation to have access to antibiotics and other effective medications. On one
hand, these advances have the potential to
improve the health of boomers and reduce
their need for healthcare services. On the
other hand, these advantages have also con-
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
1
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
tributed to longer life expectancy and improvements in
survival. As a result, people are living longer with the
potential of developing multiple chronic conditions and
hence needing more healthcare services.15 16
Parallel to these changes, baby boomers and succeeding generations have also been part of a shift to
consumer-driven healthcare where people define themselves first as consumers and then as patients. This consumer market has positioned health as an individual
right, and as a result, many people have proactive behaviours towards their health decisions and selection of services.17–19 Boomers are often avid consumers of health
information and are more willing to try new treatments
than previous generations.20 21 Yet, how changes in prosperity, medical care improvements and the rise in
medical consumerism affect baby boomers’ use of
health services remains to be examined. Studies have
not investigated whether there are generational differences in healthcare use, including consultations with
primary physicians and specialists. Formulating policy
changes and interventions to accommodate the needs of
this large cohort will require a thorough understanding
of these patterns and the diverse factors affecting healthcare use in boomers and other cohorts.
Andersen and Newman’s behavioural model of health
services is useful for identifying factors related to healthcare use.22 23 In their framework, healthcare use is conceptualised as a function of predisposing (eg, age, sex,
education), enabling (eg, income, regular source of
care) and need (eg, chronic health conditions) factors.
Behaviour-related risk factors (eg, obesity) can also be
included in the framework. Previous research has found
cohort differences related to a number of factors relevant to healthcare use of baby boomers and other
cohorts. For example, improvements in the standard of
living and education attainment since the 1950s24 25
might be expected to reduce the need for healthcare
among baby boomers and succeeding generations.
Declines in smoking rates in recent cohorts26–28 are also
likely to be related to better health and reduced healthcare.29 30 However, trends of increased obesity and sedentary lifestyles in each succeeding recent cohort29 31–34
are risk factors for worse health and may result in
increased healthcare use.30 35 36 Few studies have explicitly compared need factors like chronic health conditions across cohorts. An Australian study found that
Generation X (Gen Xers) reported more diabetes than
baby boomers34 and a study from the UK37 found that
boomers had more hypertension and diabetes than
their predecessors. In contrast, a study of US women
found no differences in arthritis prevalence between
baby boomers and the previous generation.38
Given the lack of evidence on patterns of healthcare
use among baby boomers compared to other generations, this study uses longitudinal panel data spanning
18 years to compare use of physician services ( primary
care and specialist care) across five birth cohorts: Gen X
(born: 1965–1974), Younger Baby Boomers (born: 1955–
2
1964), Older Baby Boomers (born: 1945–1954), World
War II (born: 1935–1944) and pre-World War II (born:
1925–1934). The overall goal was to (1) compare primary
care and specialist services use over the lifecourse across
birth cohorts and (2) to examine cohort differences in
predisposing, enabling, need and behaviour-related risk
factors that could explain cohort differences in the lifecourse trajectories of primary care and specialist use.
METHODS
Study setting and population
We used data from the longitudinal component of the
Canadian National Population Health Survey (NPHS)
spanning 18 years from 1994 to 2011. The NPHS, established in 1994–1995 (cycle 1), is a representative sample of
the household population residing in Canada’s 10 provinces. The survey excluded persons living on Indian
Reserves and Crown Lands, residents of health institutions,
full-time members of the Canadian Forces Bases and some
remote areas in Ontario and Québec. The NPHS retained
individuals who moved to long-term care institutions and
those who died over the course of the survey.39 We
included participants who were between 20 and 69 years
old in 1994, contributed to at least three cycles of data and
had complete information about the outcomes at baseline
(1994). This resulted in a sample of 10 186 individuals
with an average of seven cycles of data. The University of
Toronto Ethics Committee approved the study.
Data sharing
The survey is not publicly available and authorisation
from Statistics Canada is required to access the data.
Therefore, there are no additional data available.
Primary outcomes
At each cycle, participants were asked about their use of
healthcare in the previous 12 months. Canada has a
national healthcare policy which provides universal coverage for all medically necessary hospital and physician services with no copayments or other patient charges.
Access to specialists is by referral from other physicians,
usually a family physician/general practitioner (FP/GP).
Participants were asked to report the number of consultations with FP/GPs or specialists (excluding eye care) in
the 12 months prior to their interview. Since our focus
was to study services for health conditions and not wellcare visits for screening and immunisation, we defined
primary care use as reporting two or more FP/GP visits
and specialist use as reporting at least one visit to a specialist. In this paper, we use the term ‘primary care’ to
have the same meaning as ‘FP/GP’. Furthermore, specialists like those in general internal medicine do not have
primary care roles in Canada.
Predictors
Cohort membership and age were based on year of
birth. Participants were allocated in five birth cohorts:
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
Gen X (born: 1965–1974), Younger Baby Boomers
(born: 1955–1964), Older Baby Boomers (born: 1945–
1954), World War II (born: 1935–1944) and pre-World
War II (born: 1925–1934). We used Andersen and
Newman’s model of healthcare use to select variables.23
Measures of predisposing factors were gender and education. Education was measured as years of schooling
and was grouped for analyses as: <12, 12–15 and 16
+years. Enabling factors were household income and
having a regular source of care. Household income was
collected at each cycle and categorised into quartiles of
the distribution at each survey year with a separate category representing missing values. We used the presence
of chronic conditions as an indicator of need for care.
At each cycle, respondents indicated yes/no to the presence of 17 chronic conditions diagnosed by a healthcare
professional. The number of chronic conditions was
grouped as: none, 1 and 2+.
We also examined behaviour-related factors: smoking,
obesity, physical activity and sedentary lifestyle.
Participants were grouped as: current smoker, former
smoker and non-smoker (those who never smoked). We
grouped body mass index as: underweight (<18.5),
normal weight (18.5–24.9), overweight (25.0–29.9),
moderate obese (30.0–34.9) and severe obese (≥35.0).40
The survey asked a series of questions about participation in physical activities like walking for exercise,
running, gardening and collected the time per week
participants usually spent walking (bicycling) to work,
school or while doing errands. Responses were used to
group individuals as physically active (during leisure
time or active commuting) versus inactive based on
Statistics Canada-derived variables.39 Finally, participants
who reported that they ‘usually sit during the day and
don’t walk around very much’ were considered to have a
sedentary lifestyle.
Statistical analysis
Comparing birth cohorts is complex because cohort differences are linked to the effects of ageing as well as
societal and environmental changes affecting the population as a whole ( period effects). Therefore, in addition
to age, it is pertinent to consider period effects (eg,
survey year), as these may obscure cohort effects unless
they are properly modelled. However, studies aiming to
estimate the effects of age, period and cohort are hindered by the identification problem; that is, age, period
and cohort are linearly dependent.41 Because of this linearity, there is no unique solution to models, including
the three effects simultaneously. As a result, they cannot
be modelled at once. One way to deal with this problem
is to directly estimate age and cohort effects (as fixed
effects) while accounting for variability across periods
(random effect) (see discussion in Bell41 and Suzuki42).
To do this, we fitted cross-classified multilevel models in
which observations were nested within individuals and
individuals were nested within time periods.
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
We started with a model with age and cohort (model 1).
In the next steps, we added predisposing, enabling and
behaviour-related factors (model 2). And finally, we
added need factors (model 3) and examined variations
in the age and cohort estimates. In all models, age was
centred at 39 years (the mean of the distribution for the
five cohorts at baseline (1994–1995)). Models were fitted
using PROC GLIMMIX from SAS V.9.3, including incomplete cases up to the point at which they drop out or died
and likelihood estimators were used that adjust for nonresponse assuming that the data are missing at random.
The significance of variables was assessed by Wald tests.
Supplementary analyses
We conducted three sets of supplementary analyses. First,
we repeated the analyses using the number of visits to
FP/GPs, to specialists and the total number of visits as the
outcomes. We also modelled primary care use defined as
having at least one visit to FP/GPs. Second, using the
number of chronic conditions as a global measure of
need for care precluded us from elucidating the effects
of individual chronic conditions in explaining cohort differences in the outcomes. Therefore, we repeated the
analysis 17 times by adding each individual chronic condition to the models and examined changes in the
cohort coefficients. Finally, we examined the impact of
attrition in our analyses by comparing the results of the
models including indicator variables identifying participants who dropped out or died before the end of the
study and the results of restricting the analyses to individuals with complete data in the nine cycles.
Patient involvement
This study is based on a population survey that did not
involve patients.
RESULTS
Descriptive
In 1994–1995, there were 10 186 participants who met
the inclusion criteria: 1384 in the pre-World War II
cohort, 1596 in the World War II cohort, 2205 Older
Baby Boomers, 2778 Younger Baby Boomers and 2223
Gen Xers. Generally, physician services use was higher in
women than men overall and for primary care and specialist use (table 1). Women reported having a regular
source of care more often than men in all cohorts, with
the exception of the pre-World War II cohort. Education
was higher for younger boomers and Gen Xers, while
older boomers had the highest income. Men reported
slightly higher household income than women in all
cohorts. Dropping out of the study was the most
common source of attrition among baby boomers and
Gen Xers and death in the pre-World War II cohort
(table 1). In preliminary analyses, we found significant
differences in the outcomes and predictors by gender;
therefore, results are presented for women and men
separately.
3
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
Table 1 Characteristics of birth cohorts at baseline (1994–1995). Canadian NPHS, 1994–2011
N
Outcomes
% physician services use
% primary care users
% specialist users
Enabling factors
Mean age
Mean years of schooling
Predisposing factors
Mean household income*
% with regular doctor
Behaviour-related factors
% smokers (current or former)
Mean BMI
% obese
% physically inactive
% sedentary
Need factors
Mean number of chronic conditions
% with 1 chronic condition
% with 2+ chronic conditions
Attrition†
% died
% dropped-out
Pre-World
War II (1925–
1934)
Women Men
World War II
(1935–1944)
Women Men
Older baby
boomer
(1945–1954)
Women Men
Younger Baby
Boomer
(1955–1964)
Women Men
Gen X (1965–
1974)
Women Men
787
597
857
739
1150
1055
1510
1268
1201
1022
75.2
69.5
31.9
66.6
59.6
30.0
69.9
64.1
31.2
57.9
51.8
24.5
67.3
60.4
31.4
51.7
46.5
19.2
71.3
63.4
33.4
48.4
43.5
17.2
76.7
69.3
34.1
43.1
38.5
13.1
63.7
10.7
63.8
10.7
53.8
11.7
53.6
11.8
43.6
13.2
43.8
13.1
33.9
13.3
34.0
13.4
24.2
13.5
24.2
13.5
40.7
94.7
45.4
93.1
54.7
93.6
59.0
88.7
59.7
90.1
62.3
80.6
53.6
89.8
56.2
78.9
49.7
87.8
54.7
70.3
54.4
26.1
18.5
44.1
17.1
80.8
26.6
18.9
49.1
19.9
53.1
26.2
16.3
41.9
17.2
76.8
26.9
16.6
39.3
21.4
56.8
25.3
16.1
38.7
22.7
71.7
26.5
14.7
46.4
22.0
62.0
24.2
11.6
42.1
20.4
63.8
26.0
11.4
48.0
21.3
59.1
23.4
10.1
47.0
22.1
54.4
24.7
10.0
55.0
18.2
1.6
29.7
43.0
1.4
32.9
38.2
1.3
29.7
34.6
1.0
36.1
25.0
0.9
31.8
21.2
0.7
29.9
17.5
0.8
28.5
18.7
0.6
30.1
13.5
0.7
26.5
18.4
0.5
26.5
10.7
30.3
19.8
48.9
21.0
11.7
20.2
19.3
25.3
5.4
23.5
6.1
23.5
1.7
28.0
3.1
30.2
1.7
34.1
2.6
37.8
*In Canadian dollars and expressed in thousands.
†Proportions calculated based on the status at the end of the study.
BMI, body mass index; Gen X, Generation X; NPHS, National Population Health Survey.
Cohort differences in healthcare use
Cohort differences in the overall pattern of physician
services use were modest and suggested less use of services by each successive recent cohort. However, these
modest differences blinded marked cohort differences
in primary care and specialist care (table 2). We therefore analysed data for primary care and specialist care
separately.
In addition, the age and cohort patterns of physician
services use were different for men and women. For
women, primary care use declined around age 40, and
then increased as they grew older; whereas for men,
primary care use increased steadily with increasing age.
Although specialist use increased with increasing age for
women and men, this increase was more marked for
men than women (figure 1A, B, respectively). In addition to age effects, we found significant cohort differences in primary care use for women but not for men
(table 2, figure 1A). Comparing women at corresponding ages indicated that Gen Xers and younger boomers
had the lowest primary care use. Likewise, there were significant cohort differences in specialist use for women
and men. In contrast to primary care use, comparing
people at the same ages there was higher specialist use
in each succeeding recent cohort (table 2, figure 1B).
4
In all models, we controlled for the potential of period
effects. We found only a minimal variability across years
for primary care use by women and no differences for
men. No significant period effects were seen for specialist use (table 2).
Explaining cohort differences
Predisposing, enabling and behavioural risk factors
There were significant associations of predisposing, enabling and behaviour-related factors with primary care and
specialist care use (tables 3 and 4, model 2) that were
somewhat attenuated once the number of chronic conditions was entered into the models (model 3). Specifically,
there were no differences in primary care use related to
education ( predisposing factor), but education was significantly associated with specialist use: women and men
with higher education were more likely to visit specialists
than those with lower education. For enabling factors,
income was significantly associated with primary care use
for men only: those in the top income quartile were less
likely to visit FP/GPs than those in the bottom quartile
(OR=0.89, 95% CI 0.81 to 0.99). Income was not significantly associated with specialist use for either women or
men. Women and men with a regular source of care were
more likely to consult with FP/GPs and see specialists.
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
Table 2 Age and cohort effects (model 1) on physician services use: results from logistic cross-classified multilevel models.
Canadian NPHS, 1994–2011
Women
Fixed effects
Age and cohort effects
Linear age‡
Birth cohort (Ref: pre-World War)
World War II
Older Baby Boomer
Younger Baby Boomer
Gen X
Random effects§
Individual
Period (survey year)
Men
Fixed effects
Age and cohort effects
Linear age
Birth cohort (Ref: Pre-World War)
World War II
Older Baby Boomer
Younger Baby Boomer
Gen X
Random effects§
Individual
Period (survey year)
Any physician use
OR (95% CI)
Primary care
OR (95% CI)
Specialist care
OR (95% CI)
0.99 (0.98 to 0.99)***
0.99 (0.98 to 1.00)***
1.01 (1.00 to 1.02)**
1.23 (1.04
1.08 (0.90
0.94 (0.76
0.91 (0.73
1.08 (0.91 to
0.96 (0.77 to
0.84 (0.63 to
0.79 (0.64 to
1.38 (1.20 to
1.49 (1.25 to
1.48 (1.19 to
1.67 (1.29 to
to 1.45)*
to 1.31)
to 1.15)
to 1.15)
1.29)
1.19)
1.10)
0.99)**
1.58)***
1.78)***
1.83)***
2.15)***
1.32 (1.28 to 1.34)***
0.01 (0.00 to 0.03)
1.39 (1.31 to 1.47)***
0.01 (0.00 to 0.01)*
0.91 (0.85 to 0.97)***
0.00 (−0.04 to 0.04)
1.03 (1.02 to 1.03)***
1.02 (1.01 to 1.03)***
1.03 (1.02 to 1.04)***
1.32 (1.10
1.36 (1.12
1.47 (1.18
1.48 (1.16
1.16 (0.97 to
1.09 (0.90 to
1.03 (0.81 to
0.99 (0.99 to
1.32 (1.11 to
1.36 (1.10 to
1.52 (1.18 to
1.73 (1.27 to
to 1.59)**
to 1.66)**
to 1.82)**
to 1.88)**
1.27 (1.11 to 1.34)***
0.01 (0.00 to 0.04)
1.39)
1.33)
1.30)
0.99)
1.37 (1.31 to 1.43)***
0.00 (−0.04 to 0.04)
1.58)***
1.69)***
1.96)***
2.37)***
0.87 (0.79 to 0.95)***
0.04 (−0.02 to 0.10)
*p<0.05, **p<0.01, ***p<0.0001.
‡Age was centred at the mean of the distribution in 1994–1995 (39 years). Models included a quadratic age term.
§Estimates are variances.
Gen X, Generation X; NPHS, National Population Health Survey.
Figure 1 Age trajectories and birth cohort for (A) primary care use and (B) specialist care use. Values are predictions from the
fixed part of models in table 2. GenX, Generation X; OBB, Older Baby Boomer; pre-WW, pre-World War II; YBB, Younger Baby
Boomer; WW2, World War II.
Behaviour-related factors were significantly associated
with primary care and specialist use. Those who reported
sedentary lifestyles and physically active women were
more likely to consult with both types of practitioners.
Smoking was not associated with primary care use, but it
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
was associated with specialist use for men: former
smokers were more likely to visit specialists than nonsmokers (OR=1.15, 95% CI 1.04 to 1.26). Obesity was not
significantly associated with specialist use, but obese individuals were more likely to see FP/GPs.
5
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
Table 3 Predisposing, enabling, behaviour-related and need factors as predictors of physician use for women: results from
logistic cross-classified multilevel models.‡ Canadian NPHS, 1994–2011
Primary care
Model 2
OR (95% CI)
Age and cohort effects
Linear age§
0.85 (0.84 to 0.86)***
Birth cohort (Ref: pre-World War)
World war II
1.04 (0.89 to 1.22)
Older Baby Boomer
0.85 (0.69 to 1.06)
Younger Baby Boomer
0.71 (0.53 to 0.96)***
Gen X
0.64 (0.44 to 0.93)***
Predisposing, enabling and behaviour-related
Education (Ref: 16+years)
12–16 years
0.96 (0.78 to 1.19)
<12 years
0.91 (0.72 to 1.15)
Income quartiles (Ref: bottom (Q1))
Q2
0.92 (0.85 to 1.00)†
Q3
0.94 (0.86 to 1.03)
Top (Q4)
0.96 (0.87 to 1.05)
Missing
0.86 (0.73 to 1.02)†
Have regular source of care
3.82 (3.45 to 4.23)***
Smokers (Ref: never)
Current
1.01 (0.91 to 1.11)
Former
1.07 (0.98 to 1.16)
BMI (Ref: normal)¶
Underweight
1.08 0.89 to 1.30)
Overweight
1.31 (1.22 to 1.42)***
Moderate obese
1.81 (1.62 to 2.01)***
Severe obese
2.10 (1.80 to 2.45)***
Physically inactive
0.90 (0.85 to 0.95)***
Sedentary lifestyle
1.09 (1.02 to 1.17)*
Need for healthcare
Chronic conditions (Ref: none)
1
2+
Model 3
OR (95% CI)
Specialist care
Model 2
OR (95% CI)
Model 3
OR (95% CI)
0.71 (0.70 to 0.73)***
1.03 (1.03 to 1.04)
0.92 (0.92 to 0.93)***
0.86 (0.54
0.68 (0.45
0.55 (0.39
0.48 (0.28
1.38
1.38
1.31
1.45
1.15 (0.92 to
1.06 (0.86 to
0.95 (0.79 to
0.97 (0.75 to
to 1.38)
to 1.01)
to 0.79)***
to 0.81)***
(1.11 to 1.71)
(1.13 to 1.68)
(1.10 to 1.56)
(1.14 to 1.83)**
1.44)
1.30)
1.15)
1.25)
0.99 (0.81 to 1.21)
0.93 (0.75 to 1.15)
0.79 (0.66 to 0.96)*
0.57 (0.46 to 0.69)***
0.81 (0.68 to 0.97)*
0.57 (0.47 to 0.70)***
0.96 (0.89
0.98 (0.90
1.00 (0.91
0.89 (0.75
3.51 (3.17
0.95
1.00
1.03
0.94
1.44
0.99 (0.92 to
1.06 (0.97 to
1.10 (1.01 to
0.98 (0.84 to
1.30 (1.17 to
to 1.05)
to 1.07)
to 1.10)
to 1.04)
to 3.89)***
(0.88 to 1.03)
(0.92 to 1.09)
(0.95 to 1.13)
(0.81 to 1.10)
(1.30 to 1.60)***
1.07)
1.15)
1.19)*
1.14)
1.44)***
0.92 (0.84 to 1.01)
1.01 (0.93 to 1.09)
1.06 (0.97 to 1.16)
1.13 (1.04 to 1.22)**
0.99 (0.91 to 1.07)
1.07 (0.99 to 1.15)†
1.08 (0.90
1.24 (1.15
1.52 (1.37
1.53 (1.32
0.90 (0.85
1.07 (1.00
1.11
1.06
1.18
1.30
0.88
1.13
1.12 (0.94 to
1.00 (0.93 to
1.01 (0.92 to
0.99 (0.87 to
0.88 (0.83 to
1.11 (1.04 to
to 1.30)
to 1.33)***
to 1.68)***
to 1.77)***
to 0.96)***
to 1.14)***
2.03 (1.89 to 2.17)***
3.30 (3.03 to 3.60)***
(0.93 to 1.33)
(0.99 to 1.14)†
(1.07 to 1.29)***
(1.15 to 1.48)***
(0.83 to 0.92)***
(1.06 to 1.21)***
1.33)
1.07)
1.11)
1.12)
0.93)***
1.18)**
1.73 (1.61 to 1.86)***
4.98 (4.49 to 5.54)***
*p<0.05, **p<0.01, ***p<0.0001, †p<0.1.
‡Models include period (survey year) as a random effect.
§Age was centred at the mean of the distribution in 1994–1995 (39 years). Models included a quadratic age term.
¶Severe obese (≥35.0), moderate obese (30.0–34.9), overweight (25.0–29.9), underweight (<18.5), normal (18.5–24.9).
BMI, body mass index; Gen X, Generation X; NPHS, National Population Health Survey.
Need factors: impact of chronic conditions
As might be expected, the presence of chronic conditions was a significant and a strong predictor of primary
care and specialist use. When we introduced the
number of chronic conditions to the models, cohort differences in specialist care use were no longer significant
(tables 3 and 4, model 3). In contrast, the opposite
effect was seen for primary care use: cohort differences
were augmented for women and became significant for
men. Because of the dramatic change in the cohort
effects, we hypothesised that there may be a differential
impact of the number of chronic conditions on primary
care use by birth cohort. To test this hypothesis, we conducted analyses that included interaction terms between
chronic condition groups with age and cohort (see
online supplementary table S1). The interactions with
age and cohort were significant. As shown in figure 2,
6
there were large cohort differences for women (figure
2A) and men (figure 2B) reporting two or more
chronic conditions. When compared at corresponding
ages, we found lower primary care use in each succeeding recent cohort. No cohort differences were seen for
those with one or no chronic conditions.
Supplementary analyses
Findings of the models examining the number of visits
to physicians were similar to our main results. Analyses
that included each individual chronic condition revealed
that cohort differences were virtually unchanged. This
suggests that having multiple conditions, and not any
specific condition, explained the cohort differences in
the age trajectories of physician service use. Our models
adjusting for drop-outs and mortality showed, as
expected, higher overall primary care and specialist use
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
Table 4 Predisposing, enabling, behaviour-related and need factors as predictors of physician use for men: results from
logistic cross-classified multilevel models.‡ Canadian NPHS, 1994–2011
Primary care
Model 2
OR (95% CI)
Age and cohort effects
Linear age§
1.08 (1.08 to
Birth cohort (Ref: pre-World War)
World War II
1.08 (0.82 to
Older Baby Boomer
0.99 (0.77 to
Younger Baby Boomer
0.86 (0.68 to
Gen X
0.79 (0.57 to
Predisposing, enabling and behaviour-related
Education (Ref: 16+years)
12–15 years
1.20 (0.98 to
<12 years
1.20 (0.96 to
Income quartiles (Ref: bottom (Q1))
Q2
0.90 (0.82 to
Q3
0.90 (0.81 to
Top (Q4)
0.84 (0.75 to
Missing
0.69 (0.56 to
Have regular source of care
3.36 (3.06 to
Smokers (Ref: never)
Current
0.96 (0.86 to
Former
1.16 (1.06 to
BMI (Ref: normal)¶
Underweight
1.38 (0.90 to
Overweight
1.14 (1.05 to
Moderate obese
1.45 (1.29 to
Severe obese
2.09 (1.72 to
Physically inactive
0.98 (0.92 to
Sedentary lifestyle
1.21 (1.12 to
Need for healthcare
Chronic conditions (Ref: none)
1
2+
Specialist care
Model 2
OR (95% CI)
Model 3
OR (95% CI)
Model 3
OR (95% CI)
1.08)*** 0.85 (0.85 to 0.85)*** 1.28 (1.27 to 1.29)*** 1.08 (1.08 to 1.09)***
1.43)
1.28)
1.09)
1.08)
0.87 (0.64 to
0.70 (0.53 to
0.54 (0.42 to
0.46 (0.32 to
1.19)
0.93)***
0.70)***
0.65)***
1.48)
1.50)
1.19 (0.98 to 1.43)
1.17 (0.96 to 1.44)
0.76 (0.63 to 0.92)**
0.6 (0.49 to 0.74)***
0.75 (0.63 to 0.90)***
0.58 (0.48 to 0.71)***
0.99)***
0.99)***
0.93)***
0.87)***
3.68)***
0.94 (0.86 to
0.94 (0.85 to
0.89 (0.81 to
0.71 (0.57 to
3.03 (2.77 to
1.01 (0.91 to
1.03 (0.93 to
0.98 (0.88 to
0.98 (0.78 to
2.14 (1.93 to
1.07 (0.97 to
1.10 (0.99 to
1.07 (0.96 to
1.01 (0.81 to
1.86 (1.67 to
1.07)
1.28)
0.92 (0.83 to 1.02)
1.09 (0.99 to 1.19)
1.01 (0.91 to 1.13)
0.97 (0.87 to 1.07)
1.21 (1.10 to 1.33)*** 1.15 (1.04 to 1.26)***
2.10)
1.23)***
1.62)***
2.54)***
1.04)
1.30)***
1.29 (0.85 to
1.12 (1.04 to
1.30 (1.17 to
1.69 (1.40 to
0.95 (0.89 to
1.14 (1.06 to
1.24 (1.04 to
1.04 (0.93 to
0.92 (0.85 to
1.30 (0.86 to
1.09 (1.02 to
1.28 (1.19 to
1.04)
1.04)
0.99)***
0.88)***
3.32)***
1.97)
1.21)***
1.45)***
2.03)***
1.01)
1.23)***
2.27 (2.11 to 2.44)***
4.04 (3.69 to 4.43)***
1.35 (1.06 to
1.36 (1.09 to
1.51 (1.21 to
1.73 (1.32 to
1.72)**
1.71)**
1.87)**
2.28)***
1.11)
1.14)
1.09)
1.23)
2.39)***
1.49)***
1.17)
1.00)†
1.97)
1.16)*
1.38)***
1.12 (0.88 to
1.00 (0.81 to
0.99 (0.81 to
1.04 (0.81 to
0.96 (0.80 to
0.91 (0.82 to
0.90 (0.83 to
1.23 (0.81 to
1.06 (1.00 to
1.21 (1.12 to
1.41)
1.24)
1.20)
1.34)
1.18)
1.22)†
1.18)
1.26)
2.06)***
1.14)
1.02)
0.97)***
1.86)
1.14)†
1.31)***
2.03 (1.87 to 2.21)***
6.86 (5.99 to 7.87)***
*p<0.05, **p<0.01, ***p<0.0001, †p<0.1.
‡Models include period (survey year) as a random effect.
§Age was centred at the mean of the distribution in 1994–1995 (39 years). Models included a quadratic age term.
¶Severe obese (≥35.0), moderate obese (30.0–34.9), overweight (25.0–29.9), underweight (<18.5), normal (18.5–24.9).
BMI, body mass index; Gen X, Generation X; NPHS, National Population Health Survey.
among those who died during follow-up, but no impact
on the effect of predisposing, enabling, need and
behaviour-related risk factors on the outcomes. Further
comparisons between those who died and those who
were alive at the end of the study indicated that,
although the age trajectory was steeper for those who
died, cohort differences and the relationships of predisposing, enabling, need and behaviour-related factors
remained unchanged. As a result, these analyses did not
change the conclusions drawn from the main findings
(tables available on request).
DISCUSSION
This is the first study to compare the lifecourse trajectories of physician visits among preboomers, baby boomers
and Gen Xers. We found a modest decrease in the
overall use of physician services in recent cohorts
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
compared to previous cohorts. Specifically, the findings
highlighted that there were different age and cohort patterns of primary care and specialist care use, suggesting
an important shift in the pattern of healthcare use over
time. Moreover, substantial cohort differences in
primary care use were revealed when our additional analyses considered the differential impact of chronic conditions on physician services use. These analyses yielded
marked cohort differences for those with multimorbidity. They showed lower primary care use in each succeeding recent cohort, so that at the same age Gen Xers
were less likely to use primary care than Younger Baby
Boomers and so on. In contrast to primary care use, we
found that younger boomers and Gen Xers were more
likely to report using specialist care. However, these
cohort differences disappeared when healthcare needs,
namely the number of chronic conditions, were taken
7
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
Figure 2 Age trajectories of primary care use by number of chronic conditions and birth cohort. Predictions from models with
interactions between chronic condition groups and age, and with birth cohort. Models included predisposing, enabling,
behavioural risk and need factors (see online supplementary table S1). GenX: Generation X; pre-WW: pre-World War II; OBB:
Older Baby Boomer; WW2: World War II; YBB: Younger Baby Boomer.
into account. Juxtaposition of these findings suggest that
there may be a shift from primary care to specialist care
in more recent cohorts (eg, Gen Xers, younger
boomers), particularly for those with multimorbidity.
Comparison with other studies
The lower primary care use for those with multimorbidity in recent cohorts is concerning for several reasons.
First, more recent cohorts (ie, younger individuals)
reported the most multimorbidity.43 It is unclear
whether this reflects positive changes to the healthcare
system with better access, earlier diagnosis and treatment
or whether it reflects poorer health in more recent generations. Evidence from previous studies suggests that
both factors may play a role.15 44 45 Second, studies have
highlighted the important role of FP/GPs in the integration and coordination of healthcare, especially for
patients with chronic conditions.45–49 Our finding that
cohort differences in specialist use were no longer
apparent after accounting for healthcare needs suggests
that the use of specialists by birth cohorts was largely
related to need for care. Of potential concern, however,
is that those with greater need for care are individuals
from recent cohorts who may be developing multimorbidity at younger ages compared to their predecessors.43
An additional concern is that specialist services typically
focus on chronic health conditions singly with the associated duplication of care and increased costs.46 This
finding highlights the need to assess the balance
between primary and specialty care to optimise healthcare delivery.
Our finding of greater use of specialists in conjunction
with the lower primary care use among those with multimorbidity may also reflect changes in patient’s preferences and expectations of more recent cohorts like Gen
Xers and Younger Baby Boomers.18 20 Alternatively, they
may also be related to changing practice patterns of FP/
GPs. Some research indicates that FP/GPs may be more
8
likely to refer younger patients to specialists for the management of their chronic conditions.50 51 It is also possible that members of recent cohorts have access to
specialist investigations and treatment that were not
available to earlier generations, which may account for
differences across cohorts. Finally, in Canada, there have
been an increase in the number of specialist relative to
the number of FP/GPs over time, which may also contribute to the higher specialist use among recent generations.52 Future research is needed to examine primary
care referrals, as well as patients’ preferences and expectations in understanding the lower primary care use by
individuals with multimorbidity.
Our study is consistent with previous research indicating greater healthcare use with older age1–3 53 and
extends these findings by accounting for cohort effects.
Predisposing, enabling and behaviour-related factors
were important predictors of overall primary care and
specialist use, but did not contribute to explaining the
cohort differences in primary care and specialist use.
Specifically, our findings of overall higher physician use
by women are in line with previous studies.1 2 Also in
keeping with past research were the findings of educational inequities in healthcare use: individuals with
greater education were more likely to have used specialist care independently of the number of chronic conditions.1–3 Income showed variable findings and was only
important for primary care use among men, such that
lower income men were more likely to visit FP/
GPs.44 53–55 Finally, similar to other studies, we found
that obese individuals, current smokers, physically active
and/or individuals with sedentary lifestyle used more
health services.30 56–59
Strengths and limitations
An advantage of this study is that longitudinal data
enabled us to compare different cohorts at the same
chronological age. The majority of the evidence on
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
healthcare use in the population derives from crosssectional studies.2 3 53 However, it is impossible to study
cohort effects in cross-sectional studies as comparing two
cohorts at the same time point means that one is older
than the other. Our approach provides an attractive
methodology as we could integrate changes in healthcare
use indicators with changes in factors associated with
healthcare use. At the same time, the study has several
limitations, particularly related to the survey’s general
methodology. Although data were collected about healthcare use and chronic conditions, there was no direct link
between the two factors. Consequently, the interpretation
of the findings is limited due to the inability to identify
the specific conditions for which individuals are consulting with physicians. In addition, the NPHS data are selfreported and the bias associated with inaccuracies and
reporting errors is unknown. It has been found that selfreports of healthcare use may underestimate actual physician visits, particularly among those with higher volumes
of visits.60 However, because we dichotomised the outcomes, we do not expect that these under-reports
affected our results and conclusions. Furthermore, additional analyses examining the number of visits provided
similar results. Another limitation is that we were not able
to examine the effect of ethnicity/cultural background as
the vast majority (93.2%) of participants identified themselves as white.39 Finally, there was attrition over the long
follow-up time. We were able to examine the impact of
mortality and loss to follow-up in our results. These analyses did not change our conclusions.
Conclusions
We found that overall cohort differences in physician
services use were modest, but when examining use of
primary and specialist care separately, cohort differences
were larger for specialist use and in the opposite direction to that of primary care use. The higher specialist
use and the lower primary care use of those with multimorbidity in recent cohorts suggest that there has been
a shift from primary to specialty care among baby
boomers and Gen Xers. Our findings underscore the
importance of research and policies addressing generational differences in healthcare practices, expectations
and preferences to ensure coordination and integration
of healthcare delivery. If the trend of greater multimorbidity, lower primary care use and higher specialist use
among recent cohort continues, the organisation and
provision of healthcare in the near future will continue
to face great challenges.
Author affiliations
1
Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
2
Krembil Research Institute, University Health Network, Toronto, Ontario,
Canada
3
Institute for Work and Health, Toronto, Ontario, Canada
4
Institute for Clinical Evaluative Science, Toronto, Ontario, Canada
5
Department of Family and Community Medicine, University of Toronto,
Toronto, Ontario, Canada
6
Department of Family and Community Medicine, St. Michael’s Hospital,
Toronto, Ontario, Canada
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
7
Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario,
Canada
Acknowledgements Richard H Glazier is supported as a Clinician Scientist in
the Department of Family and Community Medicine at the University of
Toronto and at St. Michael’s Hospital. We thank the Statistics Canada
Research Data Centres Program for providing access to the data.
Contributors MC was the lead author on this paper. Her contributions
included study design, statistical analysis and drafting of the manuscript. MG
contributed to the design of the study, interpretation of results and critically
revised the manuscript. SH-J contributed to the design, provided statistical
advice and critically commented on the manuscript’s content. RHG and EMB
are the graduate supervisors of the lead author and provided guidance on the
study design, analysis and structure of the manuscript. All authors read and
approved the final manuscript and are accountable for all aspects of the
study.
Funding This study was partially supported by a CIHR Operating Grant–
Secondary Analysis of Databases (SEC 117113).
Competing interests None declared.
Ethics approval University of Toronto Ethics Committee.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement No additional data are available.
Open Access This is an Open Access article distributed in accordance with
the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license,
which permits others to distribute, remix, adapt, build upon this work noncommercially, and license their derivative works on different terms, provided
the original work is properly cited and the use is non-commercial. See: http://
creativecommons.org/licenses/by-nc/4.0/
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
Babitsch B, Gohl D, von Lengerke T. Re-revisiting Andersen’s
behavioral model of health services use: a systematic review of
studies from 1998-2011. Psychosoc Med 2012;9:Doc11.
Glazier RH, Agha MM, Moineddin R, et al. Universal health
insurance and equity in primary care and specialist office visits: a
population-based study. Ann Fam Med 2009;7:396–405.
Manski RJ, Moeller JF, Chen H, et al. Patterns of older Americans’
health care utilization over time. Am J Public Health
2013;103:1314–24.
Alemayehu B, Warner KE. The lifetime distribution of health care
costs. Health Serv Res 2004;39:627–42.
Di Matteo L. The macro determinants of health expenditure in the
United States and Canada: assessing the impact of income, age
distribution and time. Health Policy 2005;71:23–42.
Neuman P, Cubanski J, Damico A. Medicare per capita spending by
age and service: new data highlights oldest beneficiaries. Health Aff
(Millwood) 2015;34:335–9.
Cangelosi PR. Baby boomers: are we ready for their impact on
health care? J Psychosoc Nurs Ment Health Serv 2011;49:15–7.
Frey WH. Baby boomers and the new demographics of America’s
seniors. Generations 2010;34:28–37.
Hampton T. Experts predict visits by baby boomers will soon strain
emergency departments. JAMA 2008;299:2613–14.
Keehan S, Sisko A, Truffer C, et al. Health spending projections
through 2017: the baby-boom generation is coming to medicare.
Health Aff (Millwood) 2008;27:w145–55.
Knickman JR, Snell EK. The 2030 problem: caring for aging baby
boomers. Health Serv Res 2002;37:849–84.
Cheung E. Baby boomers, generation X and social cycles, volume
1: North American long waves. Toronto: Longwave Press, 2007.
Mellor MJ, Rehr H. Baby boomers: can my eighties be like my
fifties? 1st edn. New York (NY): Springer, 2005.
Wister AV. Baby boomer health dynamics: how are we aging?
Toronto: Toronto University Press, 2005.
Howard DH, Thorpe KE, Busch SH. Understanding recent increases
in chronic disease treatment rates: more disease or more detection?
Health Econ Policy Law 2010;5:411–35.
Crimmins EM, Beltrán-Sánchez H. Mortality and morbidity trends: is
there compression of morbidity? J Gerontol B Psychol Sci Soc Sci
2011;66:75–86.
9
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Open Access
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
10
Sulik GA, Eich-Krohm A. No longer a patient: the social construction
of the medical consumer. In: Goldner M, Chambré SM, eds. Bingley,
UK: Emerald Group Publishing Limited, 2008:3–28.
Foster MM, Earl PE, Haines TP, et al. Unravelling the concept of
consumer preference: implications for health policy and optimal
planning in primary care. Health Policy 2010;97:105–12.
Rosenthal M, Schlesinger M. Not afraid to blame: the neglected role
of blame attribution in medical consumerism and some implications
for health policy. Milbank Q 2002;80:41–94.
Kahana E, Kahana B. Baby boomers’ expectations of health and
medicine. Virtual Mentor 2014;16:380–4.
Pruchno R. Not your mother’s old age: baby boomers at age 65.
Gerontologist 2012;52:149–52.
Andersen R, Newman JF. Societal and individual determinants of
medical care utilization in the United States. Milbank Mem Fund
Q Health Soc 1973;51:95–124.
Andersen RM. National health surveys and the behavioral model of
health services use. Med Care 2008;46:647–53.
Roberts L, Clifton RA, Ferguson B, et alRecent social trends in Canada,
1960–2000. Montreal: McGill-Queen’s University Press, 2005.
The Conference Board of Canada. How Canada performs: A Report
card on Canada. http://www.conferenceboard.ca/Libraries/PUBLIC_
PDFS/13-260_HCP2013_ExecSumm.sflb
Chen X, Lin F, Stanton B, et al. Apc modeling of smoking
prevalence among US adolescents and young adults. Am J Health
Behav 2011;35:416–27.
Midlöv P, Calling S, Sundquist J, et al. The longitudinal age and
birth cohort trends of smoking in Sweden: a 24-year follow-up study.
Int J Public Health 2014;59:243–50.
Piontek D, Kraus L, Muller S, et al. To what extent do age, period,
and cohort patterns account for time trends and social inequalities in
smoking? SUCHT 2010;56:361–71.
Badley EM, Canizares M, Perruccio AV, et al. Benefits gained,
benefits lost: comparing baby boomers to other generations in a
longitudinal cohort study of self-rated health. Milbank Q
2015;93:40–72.
Azagba S, Sharaf MF, Xiao Liu C. Disparities in health care
utilization by smoking status in Canada. Int J Public Health
2013;58:913–25.
Allman-Farinelli MA, Chey T, Merom D, et al. The effects of age,
birth cohort and survey period on leisure-time physical activity by
Australian adults: 1990–2005. Br J Nutr 2009;101:609–17.
Reither EN, Hauser RM, Yang Y. Do birth cohorts matter?
Age-period-cohort analyses of the obesity epidemic in the United
States. Soc Sci Med 2009;69:1439–48.
Robinson WR, Keyes KM, Utz RL, et al. Birth cohort effects among
US-born adults born in the 1980s: foreshadowing future trends in US
obesity prevalence. Int J Obes (Lond) 2013;37:448–54.
Pilkington R, Taylor AW, Hugo G, et al. Are baby boomers healthier
than generation x? A profile of Australia’s working generations using
national health survey data. PLoS ONE 2014;9:e93087.
Allen L, Thorpe K, Joski P. The effect of obesity and chronic
conditions on medicare spending, 1987–2011. PharmacoEconomics
2015;33:691–7.
Booth HP, Prevost AT, Gulliford MC. Impact of body mass index on
prevalence of multimorbidity in primary care: cohort study. Fam Pract
2014;31:38–43.
Rice NE, Lang IA, Henley W, et al. Baby boomers nearing
retirement: the healthiest generation? Rejuvenation Res
2010;13:105–14.
Leveille SG, Wee CC, Iezzoni LI. Trends in obesity and arthritis
among baby boomers and their predecessors, 1971–2002.
Am J Public Health 2005;95:1607–13.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
Statistics Canada. Information about the National Population Health
Survey. Ottawa: Statistics Canada, 2011.
WHO. The use and interpretation of anthropometry report of a WHO
expert committee technical report series, no 854. Geneva: WHO, 1995.
Bell A. Life-course and cohort trajectories of mental health in the
UK, 1991–2008—a multilevel age-period-cohort analysis. Soc Sci
Med 2014;120:21–30.
Suzuki E. Time changes, so do people. Soc Sci Med
2012;75:452–6; discussion 57–8.
Canizares M, Hogg-Johnson S, Gignac MA, et al. Lifecourse
trajectories of multimorbidity in Canada: birth cohort differences and
predictors. Paper presented at the 2016 Epidemiology Congress of
the Americas Session: Aging Isn’t Easy, and Neither is Analyzing
Aging-Related Health Data: Novel Risk Factors and Applied
Methods Miami, USA, 2016.
Bähler C, Huber CA, Brüngger B, et al. Multimorbidity, health care
utilization and costs in an elderly community-dwelling population: a
claims data based observational study. BMC Health Serv Res
2015;15:23.
Muggah E, Graves E, Bennett C, et al. The impact of multiple
chronic diseases on ambulatory care use; a population based study
in Ontario, Canada. BMC Health Serv Res 2012;12:452.
Barnett K, Mercer SW, Norbury M, et al. Epidemiology of
multimorbidity and implications for health care, research, and
medical education: a cross-sectional study. Lancet 2012;380:37–43.
Kirby MJL. Reforming health protection and promotion in Canada:
time to act, 2002. http://www.parl.gc.ca/Content/SEN/Committee/
372/soci/rep/repfinnov03-e.htm (accessed 26 Jan 2016).
Roland M, Guthrie B, Thomé DC. Primary medical care in the United
Kingdom. J Am Board Fam Med 2012;25(Suppl 1):S6–11.
Phillips RL Jr, Bazemore AW. Primary care and why it matters for
US. Health system reform. Health Aff (Millwood) 2010;29:806–10.
Adelman RD, Capello CF, LoFaso V, et al. Introduction to the older
patient: a “first exposure” to geriatrics for medical students. J Am
Geriatr Soc 2007;55:1445–50.
Band-Winterstein T. Health care provision for older persons: the
interplay between ageism and elder neglect. J Appl Gerontol
2015;34:NP113–27.
Canadian Medical Association. Number and changing demographics
of Canada’s physicians over the years. https://www.cma.ca/En/
Pages/physician-historical-data.aspx
Jiménez-Rubio D, Smith PC, Van Doorslaer E. Equity in health and
health care in a decentralised context: evidence from Canada.
Health Econ 2008;17:377–92.
Allin S. Does equity in healthcare use vary across Canadian
provinces? Healthc Policy 2008;3:83–99.
Beckman A, Anell A. Changes in health care utilisation following
a reform involving choice and privatisation in Swedish primary care:
a five-year follow-up of GP visits. BMC Health Serv Res
2013;13:452.
Peterson MD, Mahmoudi E. Healthcare utilization associated with
obesity and physical disabilities. Am J Prev Med 2015;48:426–35.
Dogra S, Baker J, Ardern CI. The role of physical activity and body
mass index in the health care use of adults with asthma. Ann Allergy
Asthma Immunol 2009;102:462–8.
Leigh JP, Hubert HB, Romano PS. Lifestyle risk factors predict
healthcare costs in an aging cohort. Am J Prev Med
2005;29:379–87.
Atlantis E, Lange K, Wittert GA. Chronic disease trends due to
excess body weight in Australia. Obes Rev 2009;10:543–53.
Reijneveld SA, Stronks K. The validity of self-reported use of health
care across socioeconomic strata: a comparison of survey and
registration data. Int J Epidemiol 2001;30:1407–14.
Canizares M, et al. BMJ Open 2016;6:e013276. doi:10.1136/bmjopen-2016-013276
Downloaded from http://bmjopen.bmj.com/ on June 17, 2017 - Published by group.bmj.com
Do baby boomers use more healthcare
services than other generations?
Longitudinal trajectories of physician service
use across five birth cohorts
Mayilee Canizares, Monique Gignac, Sheilah Hogg-Johnson, Richard H
Glazier and Elizabeth M Badley
BMJ Open 2016 6:
doi: 10.1136/bmjopen-2016-013276
Updated information and services can be found at:
http://bmjopen.bmj.com/content/6/9/e013276
These include:
References
This article cites 49 articles, 8 of which you can access for free at:
http://bmjopen.bmj.com/content/6/9/e013276#BIBL
Open Access
This is an Open Access article distributed in accordance with the Creative
Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work
non-commercially, and license their derivative works on different terms,
provided the original work is properly cited and the use is
non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Email alerting
service
Receive free email alerts when new articles cite this article. Sign up in the
box at the top right corner of the online article.
Topic
Collections
Articles on similar topics can be found in the following collections
General practice / Family practice (629)
Health services research (1392)
Notes
To request permissions go to:
http://group.bmj.com/group/rights-licensing/permissions
To order reprints go to:
http://journals.bmj.com/cgi/reprintform
To subscribe to BMJ go to:
http://group.bmj.com/subscribe/